Balloon Detection using Mask RCNN
Instance Segmentation with Mask RCNN using Detectron2 and Pytorch
• Soumik Rakshit • 120 min read
computervision deeplearning segmentation objectdetction neuralnetwork instancesegmentation convolution detectron maskrcnn python pytorch
!pip install -U torch torchvision
!pip install git+https://github.com/facebookresearch/fvcore.git
!git clone https://github.com/facebookresearch/detectron2 detectron2_repo
!pip install -e detectron2_repo
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Installing collected packages: tqdm, detectron2
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import detectron2, cv2, random
import os, json, itertools
import numpy as np
import torch, torchvision
from detectron2.utils.logger import setup_logger
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog
from detectron2.structures import BoxMode
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.engine import DefaultTrainer
from detectron2.config import get_cfg
from matplotlib import pyplot as plt
from google.colab.patches import cv2_imshow
setup_logger()
<Logger detectron2 (DEBUG)>
!wget http://images.cocodataset.org/val2017/000000439715.jpg -O input.jpg
im = cv2.imread("./input.jpg")
cv2_imshow(im)
--2019-10-11 09:46:46-- http://images.cocodataset.org/val2017/000000439715.jpg Resolving images.cocodataset.org (images.cocodataset.org)... 52.216.106.116 Connecting to images.cocodataset.org (images.cocodataset.org)|52.216.106.116|:80... connected. HTTP request sent, awaiting response... 200 OK Length: 209222 (204K) [image/jpeg] Saving to: ‘input.jpg’ input.jpg 100%[===================>] 204.32K 847KB/s in 0.2s 2019-10-11 09:46:47 (847 KB/s) - ‘input.jpg’ saved [209222/209222]
cfg = get_cfg()
# get model from https://github.com/facebookresearch/detectron2/tree/master/configs
cfg.merge_from_file("./detectron2_repo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set threshold for this model
# Weights ke pkl kre -_-
cfg.MODEL.WEIGHTS = "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl"
predictor = DefaultPredictor(cfg)
outputs = predictor(im)
WARNING [10/11 09:46:54 d2.config.compat]: Config './detectron2_repo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml' has no VERSION. Assuming it to be compatible with latest v2.
# look at the outputs
print('Classes of instances:', outputs["instances"].pred_classes)
print('Bboxes of instances:', outputs["instances"].pred_boxes)
Classes of instances: tensor([17, 0, 0, 0, 0, 0, 0, 0, 25, 0, 25, 25, 0, 0, 24],
device='cuda:0')
Bboxes of instances: Boxes(tensor([[126.6035, 244.8977, 459.8291, 480.0000],
[251.1083, 157.8127, 338.9731, 413.6379],
[114.8496, 268.6864, 148.2352, 398.8111],
[ 0.8217, 281.0327, 78.6073, 478.4210],
[ 49.3954, 274.1229, 80.1545, 342.9808],
[561.2248, 271.5816, 596.2755, 385.2552],
[385.9072, 270.3125, 413.7130, 304.0397],
[515.9295, 278.3744, 562.2792, 389.3802],
[335.2409, 251.9167, 414.7491, 275.9375],
[350.9300, 269.2060, 386.0984, 297.9081],
[331.6292, 230.9996, 393.2759, 257.2009],
[510.7349, 263.2656, 570.9865, 295.9194],
[409.0841, 271.8646, 460.5582, 356.8722],
[506.8766, 283.3257, 529.9404, 324.0392],
[594.5663, 283.4820, 609.0577, 311.4124]], device='cuda:0'))
# They also have visualization utils :P
v = Visualizer(im, MetadataCatalog.get("coco_2017_val"), scale = 1.5)
v = v.draw_instance_predictions(outputs["instances"].to("cpu"))
cv2_imshow(v.get_image()[:, :, ::-1])
!wget https://github.com/matterport/Mask_RCNN/releases/download/v2.1/balloon_dataset.zip
!unzip balloon_dataset.zip > /dev/null
--2019-10-11 09:47:11-- https://github.com/matterport/Mask_RCNN/releases/download/v2.1/balloon_dataset.zip Resolving github.com (github.com)... 140.82.118.3 Connecting to github.com (github.com)|140.82.118.3|:443... connected. HTTP request sent, awaiting response... 302 Found Location: https://github-production-release-asset-2e65be.s3.amazonaws.com/107595270/737339e2-2b83-11e8-856a-188034eb3468?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20191011%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20191011T094712Z&X-Amz-Expires=300&X-Amz-Signature=7d6e77a1f62604e8eeea14871837711e05770f7ccefe7876722acc45d79590c6&X-Amz-SignedHeaders=host&actor_id=0&response-content-disposition=attachment%3B%20filename%3Dballoon_dataset.zip&response-content-type=application%2Foctet-stream [following] --2019-10-11 09:47:13-- https://github-production-release-asset-2e65be.s3.amazonaws.com/107595270/737339e2-2b83-11e8-856a-188034eb3468?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20191011%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20191011T094712Z&X-Amz-Expires=300&X-Amz-Signature=7d6e77a1f62604e8eeea14871837711e05770f7ccefe7876722acc45d79590c6&X-Amz-SignedHeaders=host&actor_id=0&response-content-disposition=attachment%3B%20filename%3Dballoon_dataset.zip&response-content-type=application%2Foctet-stream Resolving github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)... 52.216.165.179 Connecting to github-production-release-asset-2e65be.s3.amazonaws.com (github-production-release-asset-2e65be.s3.amazonaws.com)|52.216.165.179|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 38741381 (37M) [application/octet-stream] Saving to: ‘balloon_dataset.zip.1’ balloon_dataset.zip 100%[===================>] 36.95M 23.9MB/s in 1.5s 2019-10-11 09:47:15 (23.9 MB/s) - ‘balloon_dataset.zip.1’ saved [38741381/38741381] replace balloon/train/via_region_data.json? [y]es, [n]o, [A]ll, [N]one, [r]ename: n replace __MACOSX/balloon/train/._via_region_data.json? [y]es, [n]o, [A]ll, [N]one, [r]ename: n replace balloon/train/53500107_d24b11b3c2_b.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: n replace __MACOSX/balloon/train/._53500107_d24b11b3c2_b.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename:
def get_balloon_dicts(img_dir):
json_file = os.path.join(img_dir, "via_region_data.json")
with open(json_file) as f:
imgs_anns = json.load(f)
dataset_dicts = []
for _, v in imgs_anns.items():
record = {}
filename = os.path.join(img_dir, v["filename"])
height, width = cv2.imread(filename).shape[:2]
record["file_name"] = filename
record["height"] = height
record["width"] = width
annos = v["regions"]
objs = []
for _, anno in annos.items():
assert not anno["region_attributes"]
anno = anno["shape_attributes"]
px = anno["all_points_x"]
py = anno["all_points_y"]
poly = [(x + 0.5, y + 0.5) for x, y in zip(px, py)]
poly = list(itertools.chain.from_iterable(poly))
obj = {
"bbox": [np.min(px), np.min(py), np.max(px), np.max(py)],
"bbox_mode": BoxMode.XYXY_ABS,
"segmentation": [poly],
"category_id": 0,
"iscrowd": 0
}
objs.append(obj)
record["annotations"] = objs
dataset_dicts.append(record)
return dataset_dicts
get_balloon_dicts("./balloon/train")
[{'annotations': [{'bbox': [994, 619, 1445, 1166],
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{'annotations': [{'bbox': [534, 365, 871, 811],
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# Register Balloon Dataset
for d in ["train", "val"]:
DatasetCatalog.register("balloon/" + d, lambda d=d: get_balloon_dicts("balloon/" + d))
MetadataCatalog.get("balloon/" + d).set(thing_classes=["balloon"])
balloon_metadata = MetadataCatalog.get("balloon/train")
# Visualize Dataset
dataset_dicts = get_balloon_dicts("balloon/train")
for d in random.sample(dataset_dicts, 3):
img = cv2.imread(d["file_name"])
visualizer = Visualizer(img[:, :, ::-1], metadata=balloon_metadata, scale=0.5)
vis = visualizer.draw_dataset_dict(d)
cv2_imshow(vis.get_image()[:, :, ::-1])
# Set the training Configs
cfg = get_cfg()
cfg.merge_from_file("./detectron2_repo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
cfg.DATASETS.TRAIN = ("balloon/train",)
cfg.DATASETS.TEST = () # no metrics implemented for this dataset
cfg.DATALOADER.NUM_WORKERS = 2
cfg.MODEL.WEIGHTS = "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl" # initialize from model zoo
cfg.SOLVER.IMS_PER_BATCH = 2
cfg.SOLVER.BASE_LR = 0.00025
cfg.SOLVER.MAX_ITER = 300 # 300 iterations seems good enough, but you can certainly train longer
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128 # faster, and good enough for this toy dataset
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1 # only has one class (ballon)
WARNING [10/11 09:48:44 d2.config.compat]: Config './detectron2_repo/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml' has no VERSION. Assuming it to be compatible with latest v2.
# Trainer
os.makedirs(cfg.OUTPUT_DIR, exist_ok = True)
trainer = DefaultTrainer(cfg)
trainer.resume_or_load(resume = False)
trainer.train()
[10/11 09:48:55 d2.engine.defaults]: Model: GeneralizedRCNN( (backbone): FPN( (fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)) (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1)) (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1)) (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (top_block): LastLevelMaxPool() (bottom_up): ResNet( (stem): BasicStem( (conv1): Conv2d( 3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) ) (res2): Sequential( (0): BottleneckBlock( (shortcut): Conv2d( 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv1): Conv2d( 64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv2): Conv2d( 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv3): Conv2d( 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) ) (1): BottleneckBlock( (conv1): Conv2d( 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv2): Conv2d( 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv3): Conv2d( 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) ) (2): BottleneckBlock( (conv1): Conv2d( 256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv2): Conv2d( 64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05) ) (conv3): Conv2d( 64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) ) ) (res3): Sequential( (0): BottleneckBlock( (shortcut): Conv2d( 256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv1): Conv2d( 256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv2): Conv2d( 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv3): Conv2d( 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) (1): BottleneckBlock( (conv1): Conv2d( 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv2): Conv2d( 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv3): Conv2d( 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) (2): BottleneckBlock( (conv1): Conv2d( 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv2): Conv2d( 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv3): Conv2d( 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) (3): BottleneckBlock( (conv1): Conv2d( 512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv2): Conv2d( 128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=128, eps=1e-05) ) (conv3): Conv2d( 128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) ) ) (res4): Sequential( (0): BottleneckBlock( (shortcut): Conv2d( 512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) (conv1): Conv2d( 512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (1): BottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (2): BottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (3): BottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (4): BottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) (5): BottleneckBlock( (conv1): Conv2d( 1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv2): Conv2d( 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05) ) (conv3): Conv2d( 256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05) ) ) ) (res5): Sequential( (0): BottleneckBlock( (shortcut): Conv2d( 1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) (conv1): Conv2d( 1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv2): Conv2d( 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv3): Conv2d( 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) ) (1): BottleneckBlock( (conv1): Conv2d( 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv2): Conv2d( 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv3): Conv2d( 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) ) (2): BottleneckBlock( (conv1): Conv2d( 2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv2): Conv2d( 512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05) ) (conv3): Conv2d( 512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05) ) ) ) ) ) (proposal_generator): RPN( (anchor_generator): DefaultAnchorGenerator( (cell_anchors): BufferList() ) (rpn_head): StandardRPNHead( (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (objectness_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1)) (anchor_deltas): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1)) ) ) (roi_heads): StandardROIHeads( (box_pooler): ROIPooler( (level_poolers): ModuleList( (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True) (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True) (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True) (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True) ) ) (box_head): FastRCNNConvFCHead( (fc1): Linear(in_features=12544, out_features=1024, bias=True) (fc2): Linear(in_features=1024, out_features=1024, bias=True) ) (box_predictor): FastRCNNOutputLayers( (cls_score): Linear(in_features=1024, out_features=2, bias=True) (bbox_pred): Linear(in_features=1024, out_features=4, bias=True) ) (mask_pooler): ROIPooler( (level_poolers): ModuleList( (0): ROIAlign(output_size=(14, 14), spatial_scale=0.25, sampling_ratio=0, aligned=True) (1): ROIAlign(output_size=(14, 14), spatial_scale=0.125, sampling_ratio=0, aligned=True) (2): ROIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, aligned=True) (3): ROIAlign(output_size=(14, 14), spatial_scale=0.03125, sampling_ratio=0, aligned=True) ) ) (mask_head): MaskRCNNConvUpsampleHead( (mask_fcn1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (mask_fcn2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (mask_fcn3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (mask_fcn4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (deconv): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2)) (predictor): Conv2d(256, 1, kernel_size=(1, 1), stride=(1, 1)) ) ) ) [10/11 09:48:57 d2.data.build]: Removed 0 images with no usable annotations. 61 images left. [10/11 09:48:57 d2.data.build]: Distribution of training instances among all 1 categories: | category | #instances | |:----------:|:-------------| | balloon | 255 | | | | [10/11 09:48:57 d2.data.detection_utils]: TransformGens used in training: [ResizeShortestEdge(short_edge_length=(640, 672, 704, 736, 768, 800), max_size=1333, sample_style='choice'), RandomFlip()] [10/11 09:48:57 d2.data.build]: Using training sampler TrainingSampler
'roi_heads.box_predictor.cls_score.weight' has shape (81, 1024) in the checkpoint but (2, 1024) in the model! Skipped. 'roi_heads.box_predictor.cls_score.bias' has shape (81,) in the checkpoint but (2,) in the model! Skipped. 'roi_heads.box_predictor.bbox_pred.weight' has shape (320, 1024) in the checkpoint but (4, 1024) in the model! Skipped. 'roi_heads.box_predictor.bbox_pred.bias' has shape (320,) in the checkpoint but (4,) in the model! Skipped. 'roi_heads.mask_head.predictor.weight' has shape (80, 256, 1, 1) in the checkpoint but (1, 256, 1, 1) in the model! Skipped. 'roi_heads.mask_head.predictor.bias' has shape (80,) in the checkpoint but (1,) in the model! Skipped.
[10/11 09:49:01 d2.engine.train_loop]: Starting training from iteration 0 [10/11 09:49:25 d2.utils.events]: eta: 0:05:35 iter: 19 total_loss: 2.006 loss_cls: 0.685 loss_box_reg: 0.608 loss_mask: 0.692 loss_rpn_cls: 0.021 loss_rpn_loc: 0.005 time: 1.2004 data_time: 0.0035 lr: 0.000005 max_mem: 2725M [10/11 09:49:49 d2.utils.events]: eta: 0:05:07 iter: 39 total_loss: 2.141 loss_cls: 0.650 loss_box_reg: 0.658 loss_mask: 0.663 loss_rpn_cls: 0.040 loss_rpn_loc: 0.010 time: 1.2014 data_time: 0.0038 lr: 0.000010 max_mem: 2726M [10/11 09:50:14 d2.utils.events]: eta: 0:04:53 iter: 59 total_loss: 1.734 loss_cls: 0.581 loss_box_reg: 0.583 loss_mask: 0.603 loss_rpn_cls: 0.018 loss_rpn_loc: 0.005 time: 1.2228 data_time: 0.0035 lr: 0.000015 max_mem: 2850M [10/11 09:50:38 d2.utils.events]: eta: 0:04:26 iter: 79 total_loss: 1.721 loss_cls: 0.509 loss_box_reg: 0.633 loss_mask: 0.538 loss_rpn_cls: 0.033 loss_rpn_loc: 0.007 time: 1.2148 data_time: 0.0039 lr: 0.000020 max_mem: 2850M [10/11 09:51:03 d2.utils.events]: eta: 0:04:04 iter: 99 total_loss: 1.681 loss_cls: 0.442 loss_box_reg: 0.700 loss_mask: 0.479 loss_rpn_cls: 0.031 loss_rpn_loc: 0.008 time: 1.2220 data_time: 0.0036 lr: 0.000025 max_mem: 2850M [10/11 09:51:29 d2.utils.events]: eta: 0:03:42 iter: 119 total_loss: 1.519 loss_cls: 0.408 loss_box_reg: 0.611 loss_mask: 0.401 loss_rpn_cls: 0.028 loss_rpn_loc: 0.004 time: 1.2303 data_time: 0.0036 lr: 0.000030 max_mem: 2850M [10/11 09:51:54 d2.utils.events]: eta: 0:03:18 iter: 139 total_loss: 1.490 loss_cls: 0.370 loss_box_reg: 0.696 loss_mask: 0.364 loss_rpn_cls: 0.018 loss_rpn_loc: 0.011 time: 1.2313 data_time: 0.0036 lr: 0.000035 max_mem: 2850M [10/11 09:52:18 d2.utils.events]: eta: 0:02:53 iter: 159 total_loss: 1.293 loss_cls: 0.307 loss_box_reg: 0.611 loss_mask: 0.303 loss_rpn_cls: 0.021 loss_rpn_loc: 0.006 time: 1.2321 data_time: 0.0034 lr: 0.000040 max_mem: 2850M [10/11 09:52:43 d2.utils.events]: eta: 0:02:30 iter: 179 total_loss: 1.221 loss_cls: 0.279 loss_box_reg: 0.629 loss_mask: 0.287 loss_rpn_cls: 0.024 loss_rpn_loc: 0.011 time: 1.2337 data_time: 0.0035 lr: 0.000045 max_mem: 2850M [10/11 09:53:08 d2.utils.events]: eta: 0:02:05 iter: 199 total_loss: 1.203 loss_cls: 0.242 loss_box_reg: 0.639 loss_mask: 0.241 loss_rpn_cls: 0.020 loss_rpn_loc: 0.006 time: 1.2365 data_time: 0.0035 lr: 0.000050 max_mem: 2850M [10/11 09:53:34 d2.utils.events]: eta: 0:01:40 iter: 219 total_loss: 1.109 loss_cls: 0.234 loss_box_reg: 0.659 loss_mask: 0.205 loss_rpn_cls: 0.015 loss_rpn_loc: 0.011 time: 1.2379 data_time: 0.0035 lr: 0.000055 max_mem: 2850M [10/11 09:53:59 d2.utils.events]: eta: 0:01:16 iter: 239 total_loss: 1.023 loss_cls: 0.202 loss_box_reg: 0.596 loss_mask: 0.186 loss_rpn_cls: 0.021 loss_rpn_loc: 0.009 time: 1.2428 data_time: 0.0034 lr: 0.000060 max_mem: 2850M [10/11 09:54:24 d2.utils.events]: eta: 0:00:51 iter: 259 total_loss: 0.989 loss_cls: 0.192 loss_box_reg: 0.599 loss_mask: 0.179 loss_rpn_cls: 0.020 loss_rpn_loc: 0.007 time: 1.2424 data_time: 0.0035 lr: 0.000065 max_mem: 2850M [10/11 09:54:49 d2.utils.events]: eta: 0:00:26 iter: 279 total_loss: 0.944 loss_cls: 0.155 loss_box_reg: 0.564 loss_mask: 0.151 loss_rpn_cls: 0.026 loss_rpn_loc: 0.011 time: 1.2431 data_time: 0.0034 lr: 0.000070 max_mem: 2850M [10/11 09:55:17 d2.utils.events]: eta: 0:00:01 iter: 299 total_loss: 0.761 loss_cls: 0.135 loss_box_reg: 0.468 loss_mask: 0.119 loss_rpn_cls: 0.017 loss_rpn_loc: 0.006 time: 1.2489 data_time: 0.0035 lr: 0.000075 max_mem: 2850M [10/11 09:55:18 d2.engine.hooks]: Overall training speed: 297 iterations in 0:06:12 (1.2531 s / it) [10/11 09:55:18 d2.engine.hooks]: Total training time: 0:06:14 (0:00:02 on hooks)
OrderedDict()
# Save model for testing
cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "model_final.pth")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7 # set the testing threshold for this model
cfg.DATASETS.TEST = ("balloon_val", )
predictor = DefaultPredictor(cfg)
# Inference on Validation Set
from detectron2.utils.visualizer import ColorMode
dataset_dicts = get_balloon_dicts("balloon/val")
for d in random.sample(dataset_dicts, 3):
im = cv2.imread(d["file_name"])
outputs = predictor(im)
v = Visualizer(im[:, :, ::-1],
metadata=MetadataCatalog.get("balloon_val"),
scale=0.8,
instance_mode=ColorMode.IMAGE_BW # remove the colors of unsegmented pixels
)
v = v.draw_instance_predictions(outputs["instances"].to("cpu"))
cv2_imshow(v.get_image()[:, :, ::-1])
